Introduction

Consumer Behavior in Pullman, Washington

Start by looking at quantity and mix of products sold in Pullman, comparing the school year and summertime.

Overall total items sold

Percent of sales that are extracts for inhalation
Percent of products sold that are edibles
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Perc Product by Big City, Noncollege-Nonurban, and Pullman

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## 
## Call:
## lm(formula = perc.usable ~ college + beforeSummer + college_before, 
##     data = did_pullman_seattle)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.043052 -0.019305  0.002384  0.015831  0.049189 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.719350   0.009524  75.529   <2e-16 ***
## college         0.005346   0.013469   0.397    0.695    
## beforeSummer   -0.018644   0.013469  -1.384    0.179    
## college_before  0.026223   0.019048   1.377    0.181    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0252 on 24 degrees of freedom
## Multiple R-squared:  0.1997, Adjusted R-squared:  0.09965 
## F-statistic: 1.996 on 3 and 24 DF,  p-value: 0.1414
## 
## Call:
## lm(formula = tot_items ~ college + beforeSummer + college_before, 
##     data = did_pullman_seattle)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4452.1  -341.6   -64.6   259.6  5245.9 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     16589.1      909.3  18.244 1.42e-15 ***
## college        -15836.7     1285.9 -12.316 7.29e-12 ***
## beforeSummer     3317.0     1285.9   2.580   0.0164 *  
## college_before  -3314.3     1818.5  -1.822   0.0809 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2406 on 24 degrees of freedom
## Multiple R-squared:  0.9401, Adjusted R-squared:  0.9326 
## F-statistic: 125.6 on 3 and 24 DF,  p-value: 8.321e-15
## 
## Call:
## lm(formula = perc.extracts ~ college + beforeSummer + college_before, 
##     data = did_pullman_seattle)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -0.0238026 -0.0033981 -0.0000447  0.0036002  0.0298770 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.111405   0.004454  25.014  < 2e-16 ***
## college        -0.014757   0.006298  -2.343 0.027751 *  
## beforeSummer    0.001647   0.006298   0.261 0.795940    
## college_before  0.036798   0.008907   4.131 0.000378 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.01178 on 24 degrees of freedom
## Multiple R-squared:  0.6129, Adjusted R-squared:  0.5645 
## F-statistic: 12.67 on 3 and 24 DF,  p-value: 3.659e-05
## 
## Call:
## lm(formula = perc.edible ~ college + beforeSummer + college_before, 
##     data = did_pullman_seattle)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.033336 -0.015386 -0.002612  0.013170  0.033015 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.131097   0.007554  17.356 4.36e-15 ***
## college        -0.018924   0.010682  -1.772   0.0892 .  
## beforeSummer    0.011427   0.010682   1.070   0.2954    
## college_before -0.039443   0.015107  -2.611   0.0153 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.01998 on 24 degrees of freedom
## Multiple R-squared:  0.5876, Adjusted R-squared:  0.5361 
## F-statistic:  11.4 on 3 and 24 DF,  p-value: 7.669e-05
## 
## Call:
## lm(formula = perc.usable ~ college + beforeSummer + college_before, 
##     data = did_pullman_spokane)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.043052 -0.012561  0.001369  0.013180  0.049189 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.716946   0.008565  83.705   <2e-16 ***
## college         0.015329   0.012113   1.266    0.218    
## beforeSummer    0.009995   0.012113   0.825    0.417    
## college_before -0.017574   0.017130  -1.026    0.315    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02266 on 24 degrees of freedom
## Multiple R-squared:  0.06454,    Adjusted R-squared:  -0.0524 
## F-statistic: 0.5519 on 3 and 24 DF,  p-value: 0.6518
## 
## Call:
## lm(formula = tot_items ~ college + beforeSummer + college_before, 
##     data = did_pullman_spokane)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1158.43  -464.46   -91.29   114.61  1739.57 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      7017.4      299.9  23.396  < 2e-16 ***
## college         -6262.3      424.2 -14.763 1.53e-13 ***
## beforeSummer     -992.9      424.2  -2.341   0.0279 *  
## college_before    990.1      599.9   1.651   0.1119    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 793.6 on 24 degrees of freedom
## Multiple R-squared:  0.9399, Adjusted R-squared:  0.9324 
## F-statistic: 125.1 on 3 and 24 DF,  p-value: 8.738e-15
## 
## Call:
## lm(formula = perc.extracts ~ college + beforeSummer + college_before, 
##     data = did_pullman_spokane)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.023803 -0.010274 -0.002200  0.008437  0.029877 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.168860   0.005192  32.521  < 2e-16 ***
## college        -0.033767   0.007343  -4.598 0.000115 ***
## beforeSummer    0.003008   0.007343   0.410 0.685678    
## college_before -0.041454   0.010385  -3.992 0.000538 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.01374 on 24 degrees of freedom
## Multiple R-squared:  0.8516, Adjusted R-squared:  0.8331 
## F-statistic: 45.91 on 3 and 24 DF,  p-value: 4.27e-10
## 
## Call:
## lm(formula = perc.edible ~ college + beforeSummer + college_before, 
##     data = did_pullman_spokane)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.033336 -0.005723 -0.000689  0.004758  0.028886 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.075373   0.005560  13.555 9.64e-13 ***
## college         0.008785   0.007864   1.117  0.27500    
## beforeSummer   -0.005670   0.007864  -0.721  0.47786    
## college_before  0.033685   0.011121   3.029  0.00579 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.01471 on 24 degrees of freedom
## Multiple R-squared:  0.5894, Adjusted R-squared:  0.5381 
## F-statistic: 11.48 on 3 and 24 DF,  p-value: 7.285e-05
## [1] "collegetown"       "saledate"          "revenue"          
## [4] "tot_items"         "avg_price_peritem" "perc.usable"      
## [7] "perc.edible"       "perc.extracts"
## 
## Call:
## lm(formula = perc.usable ~ college + beforeSummer + college_before, 
##     data = did_pullman_noncollege)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.043052 -0.014397  0.001367  0.013006  0.049189 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.714375   0.008759  81.558   <2e-16 ***
## college         0.017900   0.012387   1.445    0.161    
## beforeSummer    0.016998   0.012387   1.372    0.183    
## college_before -0.024577   0.017518  -1.403    0.173    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02317 on 24 degrees of freedom
## Multiple R-squared:    0.1,  Adjusted R-squared:  -0.01246 
## F-statistic: 0.8893 on 3 and 24 DF,  p-value: 0.4608

## 
## Call:
## lm(formula = perc.extracts ~ college + beforeSummer + college_before, 
##     data = did_pullman_noncollege)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -0.0238026 -0.0039328  0.0001344  0.0046867  0.0298770 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.1415556  0.0044484  31.821  < 2e-16 ***
## college        -0.0064627  0.0062910  -1.027 0.314526    
## beforeSummer   -0.0007699  0.0062910  -0.122 0.903618    
## college_before -0.0376756  0.0088969  -4.235 0.000291 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.01177 on 24 degrees of freedom
## Multiple R-squared:  0.7439, Adjusted R-squared:  0.7119 
## F-statistic: 23.24 on 3 and 24 DF,  p-value: 2.804e-07

## 
## Call:
## lm(formula = perc.edible ~ college + beforeSummer + college_before, 
##     data = did_pullman_noncollege)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.033336 -0.010212 -0.003450  0.008487  0.028886 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.107721   0.006312  17.067 6.33e-15 ***
## college        -0.023563   0.008926  -2.640  0.01435 *  
## beforeSummer   -0.008578   0.008926  -0.961  0.34612    
## college_before  0.036594   0.012623   2.899  0.00788 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0167 on 24 degrees of freedom
## Multiple R-squared:  0.3234, Adjusted R-squared:  0.2388 
## F-statistic: 3.824 on 3 and 24 DF,  p-value: 0.02268

## 
## Call:
## lm(formula = perc.usable ~ college + fallSemester + college_fall, 
##     data = did_pullman_noncollege_fallsem)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.053011 -0.011930  0.001334  0.009271  0.048185 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.715931   0.008923  80.233  < 2e-16 ***
## college       0.046743   0.012619   3.704  0.00111 ** 
## fallSemester  0.003667   0.012619   0.291  0.77384    
## college_fall -0.034699   0.017846  -1.944  0.06367 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02361 on 24 degrees of freedom
## Multiple R-squared:  0.4144, Adjusted R-squared:  0.3412 
## F-statistic: 5.661 on 3 and 24 DF,  p-value: 0.004439
## 
## Call:
## lm(formula = perc.extracts ~ college + fallSemester + college_fall, 
##     data = did_pullman_noncollege_fallsem)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.021738 -0.005143 -0.000400  0.003117  0.032301 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.142761   0.004147  34.428  < 2e-16 ***
## college      -0.020774   0.005864  -3.543  0.00166 ** 
## fallSemester  0.001342   0.005864   0.229  0.82089    
## college_fall  0.023657   0.008293   2.853  0.00879 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.01097 on 24 degrees of freedom
## Multiple R-squared:  0.488,  Adjusted R-squared:  0.4241 
## F-statistic: 7.626 on 3 and 24 DF,  p-value: 0.0009481
## 
## Call:
## lm(formula = perc.edible ~ college + fallSemester + college_fall, 
##     data = did_pullman_noncollege_fallsem)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.034076 -0.009247 -0.000059  0.010763  0.041184 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.097600   0.006544  14.914 1.23e-13 ***
## college      -0.012304   0.009255  -1.329    0.196    
## fallSemester -0.009509   0.009255  -1.027    0.314    
## college_fall  0.019212   0.013088   1.468    0.155    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.01731 on 24 degrees of freedom
## Multiple R-squared:  0.08831,    Adjusted R-squared:  -0.02565 
## F-statistic: 0.7749 on 3 and 24 DF,  p-value: 0.5194

Noncollege, Non Urban

## 
## Call:
## lm(formula = perc.usable ~ college + beforeSummer + college_before, 
##     data = did_pullman_noncollege_nonurban)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.043052 -0.013132  0.002439  0.012509  0.049189 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.742694   0.008523  87.139   <2e-16 ***
## college        -0.010419   0.012053  -0.864    0.396    
## beforeSummer    0.015568   0.012053   1.292    0.209    
## college_before -0.023147   0.017046  -1.358    0.187    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02255 on 24 degrees of freedom
## Multiple R-squared:  0.2666, Adjusted R-squared:  0.1749 
## F-statistic: 2.907 on 3 and 24 DF,  p-value: 0.05533
## 
## Call:
## lm(formula = perc.edible ~ college + beforeSummer + college_before, 
##     data = did_pullman_noncollege_nonurban)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.033336 -0.008104 -0.003345  0.009915  0.028886 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.089731   0.005983  14.998 1.09e-13 ***
## college        -0.005573   0.008461  -0.659  0.51636    
## beforeSummer   -0.007654   0.008461  -0.905  0.37465    
## college_before  0.035670   0.011966   2.981  0.00649 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.01583 on 24 degrees of freedom
## Multiple R-squared:  0.3997, Adjusted R-squared:  0.3247 
## F-statistic: 5.327 on 3 and 24 DF,  p-value: 0.005884
## 
## Call:
## lm(formula = perc.extracts ~ college + beforeSummer + college_before, 
##     data = did_pullman_noncollege_nonurban)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -0.0238026 -0.0037265 -0.0008578  0.0052419  0.0298770 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.136883   0.004469  30.629  < 2e-16 ***
## college        -0.001790   0.006320  -0.283 0.779407    
## beforeSummer   -0.002769   0.006320  -0.438 0.665222    
## college_before -0.035677   0.008938  -3.991 0.000538 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.01182 on 24 degrees of freedom
## Multiple R-squared:  0.7018, Adjusted R-squared:  0.6645 
## F-statistic: 18.83 on 3 and 24 DF,  p-value: 1.696e-06
## 
## Call:
## lm(formula = avg_price_peritem ~ college + beforeSummer + college_before, 
##     data = did_pullman_noncollege_nonurban)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.11647 -0.45267  0.08998  0.70174  1.59286 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     24.2816     0.3879  62.604  < 2e-16 ***
## college          3.7463     0.5485   6.830  4.6e-07 ***
## beforeSummer     0.3974     0.5485   0.724    0.476    
## college_before  -0.2763     0.7757  -0.356    0.725    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.026 on 24 degrees of freedom
## Multiple R-squared:  0.784,  Adjusted R-squared:  0.757 
## F-statistic: 29.04 on 3 and 24 DF,  p-value: 3.718e-08
## 
## Call:
## lm(formula = avg_price_peritem ~ college + beforeSummer + college_before, 
##     data = did_pullman_noncollege_nonurban)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.11647 -0.45267  0.08998  0.70174  1.59286 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     24.2816     0.3879  62.604  < 2e-16 ***
## college          3.7463     0.5485   6.830  4.6e-07 ***
## beforeSummer     0.3974     0.5485   0.724    0.476    
## college_before  -0.2763     0.7757  -0.356    0.725    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.026 on 24 degrees of freedom
## Multiple R-squared:  0.784,  Adjusted R-squared:  0.757 
## F-statistic: 29.04 on 3 and 24 DF,  p-value: 3.718e-08
## 
## Call:
## lm(formula = tot_items ~ college + beforeSummer + college_before, 
##     data = did_pullman_noncollege_nonurban)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6525.4 -1177.2  -104.4   114.6  8020.6 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       28286       1311  21.578  < 2e-16 ***
## college          -27531       1854 -14.850 1.35e-13 ***
## beforeSummer      -3956       1854  -2.134   0.0433 *  
## college_before     3953       2622   1.508   0.1447    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3468 on 24 degrees of freedom
## Multiple R-squared:  0.9413, Adjusted R-squared:  0.9339 
## F-statistic: 128.2 on 3 and 24 DF,  p-value: 6.617e-15
## 
## Call:
## lm(formula = revenue ~ college + beforeSummer + college_before, 
##     data = did_pullman_noncollege_nonurban)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -152413  -48210   -2171    5381  222191 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      686982      33927  20.249  < 2e-16 ***
## college         -665779      47980 -13.876 5.84e-13 ***
## beforeSummer     -85085      47980  -1.773   0.0889 .  
## college_before    85112      67854   1.254   0.2218    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 89760 on 24 degrees of freedom
## Multiple R-squared:  0.9342, Adjusted R-squared:  0.9259 
## F-statistic: 113.5 on 3 and 24 DF,  p-value: 2.586e-14

Fall Semester

## 
## Call:
## lm(formula = perc.usable ~ college + fallSemester + college_fall, 
##     data = did_fallsem_noncollege_nonurban)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.053011 -0.008539  0.001830  0.007296  0.048185 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.720918   0.008684  83.018  < 2e-16 ***
## college       0.041756   0.012281   3.400  0.00236 ** 
## fallSemester  0.002770   0.012281   0.226  0.82345    
## college_fall -0.033801   0.017368  -1.946  0.06343 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02298 on 24 degrees of freedom
## Multiple R-squared:  0.3787, Adjusted R-squared:  0.301 
## F-statistic: 4.876 on 3 and 24 DF,  p-value: 0.008697
## 
## Call:
## lm(formula = perc.edible ~ college + fallSemester + college_fall, 
##     data = did_fallsem_noncollege_nonurban)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.034076 -0.009811 -0.000522  0.007139  0.041184 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.087482   0.006345  13.787 6.71e-13 ***
## college      -0.002186   0.008974  -0.244    0.810    
## fallSemester -0.007769   0.008974  -0.866    0.395    
## college_fall  0.017472   0.012691   1.377    0.181    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.01679 on 24 degrees of freedom
## Multiple R-squared:  0.1106, Adjusted R-squared:  -0.0005933 
## F-statistic: 0.9947 on 3 and 24 DF,  p-value: 0.4122
## 
## Call:
## lm(formula = perc.extracts ~ college + fallSemester + college_fall, 
##     data = did_fallsem_noncollege_nonurban)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.021738 -0.004491 -0.000549  0.003308  0.032301 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.1507051  0.0041289  36.500  < 2e-16 ***
## college      -0.0287187  0.0058392  -4.918 5.11e-05 ***
## fallSemester  0.0001914  0.0058392   0.033  0.97412    
## college_fall  0.0248075  0.0082579   3.004  0.00615 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.01092 on 24 degrees of freedom
## Multiple R-squared:  0.5858, Adjusted R-squared:  0.534 
## F-statistic: 11.31 on 3 and 24 DF,  p-value: 8.073e-05
## 
## Call:
## lm(formula = avg_price_peritem ~ college + fallSemester + college_fall, 
##     data = did_fallsem_noncollege_nonurban)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.1992 -0.4471  0.1483  0.4487  1.9141 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   24.7673     0.3971  62.371  < 2e-16 ***
## college       -3.9935     0.5616  -7.111 2.38e-07 ***
## fallSemester  -1.0184     0.5616  -1.814   0.0823 .  
## college_fall   1.4477     0.7942   1.823   0.0808 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.051 on 24 degrees of freedom
## Multiple R-squared:  0.7491, Adjusted R-squared:  0.7178 
## F-statistic: 23.89 on 3 and 24 DF,  p-value: 2.194e-07
## 
## Call:
## lm(formula = tot_items ~ college + fallSemester + college_fall, 
##     data = did_fallsem_noncollege_nonurban)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7111.0 -1652.5  -117.5   145.2 10587.0 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     39166       1491  26.276  < 2e-16 ***
## college        -38061       2108 -18.056  1.8e-15 ***
## fallSemester     2523       2108   1.197    0.243    
## college_fall    -2170       2981  -0.728    0.474    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3944 on 24 degrees of freedom
## Multiple R-squared:  0.9664, Adjusted R-squared:  0.9622 
## F-statistic: 230.4 on 3 and 24 DF,  p-value: < 2.2e-16
## 
## Call:
## lm(formula = revenue ~ college + fallSemester + college_fall, 
##     data = did_fallsem_noncollege_nonurban)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -168695  -43576   -3037    2588  260398 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    969082      36849  26.299  < 2e-16 ***
## college       -946120      52112 -18.155 1.59e-15 ***
## fallSemester    22211      52112   0.426    0.674    
## college_fall   -14259      73698  -0.193    0.848    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 97490 on 24 degrees of freedom
## Multiple R-squared:  0.9654, Adjusted R-squared:  0.9611 
## F-statistic: 223.1 on 3 and 24 DF,  p-value: < 2.2e-16

Noncollege, Non Urban

## 
## Call:
## lm(formula = perc.usable ~ college + beforeSummer + college_before, 
##     data = did_pullman_noncollege_nonurban)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.043052 -0.013132  0.002439  0.012509  0.049189 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.742694   0.008523  87.139   <2e-16 ***
## college        -0.010419   0.012053  -0.864    0.396    
## beforeSummer    0.015568   0.012053   1.292    0.209    
## college_before -0.023147   0.017046  -1.358    0.187    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02255 on 24 degrees of freedom
## Multiple R-squared:  0.2666, Adjusted R-squared:  0.1749 
## F-statistic: 2.907 on 3 and 24 DF,  p-value: 0.05533
## 
## Call:
## lm(formula = perc.edible ~ college + beforeSummer + college_before, 
##     data = did_pullman_noncollege_nonurban)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.033336 -0.008104 -0.003345  0.009915  0.028886 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.089731   0.005983  14.998 1.09e-13 ***
## college        -0.005573   0.008461  -0.659  0.51636    
## beforeSummer   -0.007654   0.008461  -0.905  0.37465    
## college_before  0.035670   0.011966   2.981  0.00649 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.01583 on 24 degrees of freedom
## Multiple R-squared:  0.3997, Adjusted R-squared:  0.3247 
## F-statistic: 5.327 on 3 and 24 DF,  p-value: 0.005884
## 
## Call:
## lm(formula = perc.extracts ~ college + beforeSummer + college_before, 
##     data = did_pullman_noncollege_nonurban)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -0.0238026 -0.0037265 -0.0008578  0.0052419  0.0298770 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     0.136883   0.004469  30.629  < 2e-16 ***
## college        -0.001790   0.006320  -0.283 0.779407    
## beforeSummer   -0.002769   0.006320  -0.438 0.665222    
## college_before -0.035677   0.008938  -3.991 0.000538 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.01182 on 24 degrees of freedom
## Multiple R-squared:  0.7018, Adjusted R-squared:  0.6645 
## F-statistic: 18.83 on 3 and 24 DF,  p-value: 1.696e-06
## 
## Call:
## lm(formula = avg_price_peritem ~ college + beforeSummer + college_before, 
##     data = did_pullman_noncollege_nonurban)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.11647 -0.45267  0.08998  0.70174  1.59286 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     24.2816     0.3879  62.604  < 2e-16 ***
## college          3.7463     0.5485   6.830  4.6e-07 ***
## beforeSummer     0.3974     0.5485   0.724    0.476    
## college_before  -0.2763     0.7757  -0.356    0.725    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.026 on 24 degrees of freedom
## Multiple R-squared:  0.784,  Adjusted R-squared:  0.757 
## F-statistic: 29.04 on 3 and 24 DF,  p-value: 3.718e-08
## 
## Call:
## lm(formula = avg_price_peritem ~ college + beforeSummer + college_before, 
##     data = did_pullman_noncollege_nonurban)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.11647 -0.45267  0.08998  0.70174  1.59286 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     24.2816     0.3879  62.604  < 2e-16 ***
## college          3.7463     0.5485   6.830  4.6e-07 ***
## beforeSummer     0.3974     0.5485   0.724    0.476    
## college_before  -0.2763     0.7757  -0.356    0.725    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.026 on 24 degrees of freedom
## Multiple R-squared:  0.784,  Adjusted R-squared:  0.757 
## F-statistic: 29.04 on 3 and 24 DF,  p-value: 3.718e-08
## 
## Call:
## lm(formula = tot_items ~ college + beforeSummer + college_before, 
##     data = did_pullman_noncollege_nonurban)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6525.4 -1177.2  -104.4   114.6  8020.6 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       28286       1311  21.578  < 2e-16 ***
## college          -27531       1854 -14.850 1.35e-13 ***
## beforeSummer      -3956       1854  -2.134   0.0433 *  
## college_before     3953       2622   1.508   0.1447    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3468 on 24 degrees of freedom
## Multiple R-squared:  0.9413, Adjusted R-squared:  0.9339 
## F-statistic: 128.2 on 3 and 24 DF,  p-value: 6.617e-15
## 
## Call:
## lm(formula = revenue ~ college + beforeSummer + college_before, 
##     data = did_pullman_noncollege_nonurban)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -152413  -48210   -2171    5381  222191 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      686982      33927  20.249  < 2e-16 ***
## college         -665779      47980 -13.876 5.84e-13 ***
## beforeSummer     -85085      47980  -1.773   0.0889 .  
## college_before    85112      67854   1.254   0.2218    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 89760 on 24 degrees of freedom
## Multiple R-squared:  0.9342, Adjusted R-squared:  0.9259 
## F-statistic: 113.5 on 3 and 24 DF,  p-value: 2.586e-14